backtesting-trading-strategies

backtesting-trading-strategies

Backtesting Trading Strategies

Overview

Validate trading strategies against historical data before risking real capital. This skill provides a complete backtesting framework with 8 built-in strategies, comprehensive performance metrics, and parameter optimization.

Key Features:

• 8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)

• Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)

• Parameter grid search optimization

• Equity curve visualization

• Trade-by-trade analysis

Prerequisites

Install required dependencies:

set -euo pipefail
pip install pandas numpy yfinance matplotlib

Optional for advanced features:

set -euo pipefail
pip install ta-lib scipy scikit-learn

Instructions

• Fetch historical data (cached to ${CLAUDE_SKILL_DIR}/data/ for reuse):

python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d

• Run a backtest with default or custom parameters:

python ${CLAUDE_SKILL_DIR}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y
python ${CLAUDE_SKILL_DIR}/scripts/backtest.py \
  --strategy rsi_reversal \
  --symbol ETH-USD \
  --period 1y \
  --capital 10000 \  # 10000: 10 seconds in ms
  --params '{"period": 14, "overbought": 70, "oversold": 30}'

• Analyze results saved to ${CLAUDE_SKILL_DIR}/reports/ -- includes *_summary.txt (performance metrics), *_trades.csv (trade log), *_equity.csv (equity curve data), and *_chart.png (visual equity curve).

• Optimize parameters via grid search to find the best combination:

python ${CLAUDE_SKILL_DIR}/scripts/optimize.py \
  --strategy sma_crossover \
  --symbol BTC-USD \
  --period 1y \
  --param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}'  # HTTP 200 OK

Output

Performance Metrics

Metric Description

Total Return Overall percentage gain/loss

CAGR Compound annual growth rate

Sharpe Ratio Risk-adjusted return (target: >1.5)

Sortino Ratio Downside risk-adjusted return

Calmar Ratio Return divided by max drawdown

Risk Metrics

Metric Description

Max Drawdown Largest peak-to-trough decline

VaR (95%) Value at Risk at 95% confidence

CVaR (95%) Expected loss beyond VaR

Volatility Annualized standard deviation

Trade Statistics

Metric Description

Total Trades Number of round-trip trades

Win Rate Percentage of profitable trades

Profit Factor Gross profit divided by gross loss

Expectancy Expected value per trade

Example Output

================================================================================
                    BACKTEST RESULTS: SMA CROSSOVER
                    BTC-USD | [start_date] to [end_date]
================================================================================
 PERFORMANCE                          | RISK
 Total Return:        +47.32%         | Max Drawdown:      -18.45%
 CAGR:                +47.32%         | VaR (95%):         -2.34%
 Sharpe Ratio:        1.87            | Volatility:        42.1%
 Sortino Ratio:       2.41            | Ulcer Index:       8.2
--------------------------------------------------------------------------------
 TRADE STATISTICS
 Total Trades:        24              | Profit Factor:     2.34
 Win Rate:            58.3%           | Expectancy:        $197.17
 Avg Win:             $892.45         | Max Consec. Losses: 3
================================================================================

Supported Strategies

Strategy Description Key Parameters

sma_crossover Simple moving average crossover fast_period, slow_period

ema_crossover Exponential MA crossover fast_period, slow_period

rsi_reversal RSI overbought/oversold period, overbought, oversold

macd MACD signal line crossover fast, slow, signal

bollinger_bands Mean reversion on bands period, std_dev

breakout Price breakout from range lookback, threshold

mean_reversion Return to moving average period, z_threshold

momentum Rate of change momentum period, threshold

Configuration

Create ${CLAUDE_SKILL_DIR}/config/settings.yaml:

data:
  provider: yfinance
  cache_dir: ./data

backtest:
  default_capital: 10000  # 10000: 10 seconds in ms
  commission: 0.001     # 0.1% per trade
  slippage: 0.0005      # 0.05% slippage

risk:
  max_position_size: 0.95
  stop_loss: null       # Optional fixed stop loss
  take_profit: null     # Optional fixed take profit

Error Handling

See ${CLAUDE_SKILL_DIR}/references/errors.md for common issues and solutions.

Examples

See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed usage examples including:

• Multi-asset comparison

• Walk-forward analysis

• Parameter optimization workflows

Files

File Purpose

scripts/backtest.py Main backtesting engine

scripts/fetch_data.py Historical data fetcher

scripts/strategies.py Strategy definitions

scripts/metrics.py Performance calculations

scripts/optimize.py Parameter optimization

Resources

• yfinance - Yahoo Finance data

• TA-Lib - Technical analysis library

• QuantStats - Portfolio analytics

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